





"""simple docstring"""

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCAmelCase        ="""true"""



def        __a							(			A ,	A=82 ,	A=16 )       ->						List[Any]:



   '''simple docstring'''



   set_seed(42 )
   A__											=					RegressionModel()
   A__											=					deepcopy(_A )
   A__											=					RegressionDataset(length=_A )
   A__											=					DataLoader(_A ,	batch_size=_A )
   model.to(accelerator.device )
   A__						,    A__											=					accelerator.prepare(_A ,	_A )
   return model, ddp_model, dataloader



def        __a							(			A ,	A=False )       ->						Optional[Any]:



   '''simple docstring'''



   A__											=					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
   A__											=					load_dataset("glue" ,	"mrpc" ,	split="validation" )

   def tokenize_function(A ):
      A__											=					tokenizer(examples["sentence1"] ,	examples["sentence2"] ,	truncation=_A ,	max_length=_A )
      return outputs

   with accelerator.main_process_first():
      A__											=					dataset.map(
          _A ,	batched=_A ,	remove_columns=["idx", "sentence1", "sentence2"] ,	)

   A__											=					tokenized_datasets.rename_column("label" ,	"labels" )

   def collate_fn(A ):
      if use_longest:
         return tokenizer.pad(_A ,	padding="longest" ,	return_tensors="pt" )
      return tokenizer.pad(_A ,	padding="max_length" ,	max_length=128 ,	return_tensors="pt" )

   return DataLoader(_A ,	shuffle=_A ,	collate_fn=_A ,	batch_size=16 )



def        __a							(			A ,	A )       ->						List[str]:



   '''simple docstring'''



   A__											=					Accelerator(dispatch_batches=_A ,	split_batches=_A )
   A__											=					get_dataloader(_A ,	not dispatch_batches )
   A__											=					AutoModelForSequenceClassification.from_pretrained(
       "hf-internal-testing/mrpc-bert-base-cased" ,	return_dict=_A )
   A__						,    A__											=					accelerator.prepare(_A ,	_A )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def        __a							(			A ,	A ,	A )       ->						str:



   '''simple docstring'''



   A__											=					[]
   for batch in dataloader:
      A__						,    A__											=					batch.values()
      with torch.no_grad():
         A__											=					model(_A )
         A__						,    A__											=					accelerator.gather_for_metrics((logit, target) )
         logits_and_targets.append((logit, target) )
   A__						,    A__											=					[], []
   for logit, targ in logits_and_targets:
      logits.append(_A )
      targs.append(_A )
   A__						,    A__											=					torch.cat(_A ), torch.cat(_A )
   return logits, targs



def        __a							(			A ,	A=82 ,	A=False ,	A=False ,	A=16 )       ->						Any:



   '''simple docstring'''



   A__						,    A__						,    A__											=					get_basic_setup(_A ,	_A ,	_A )
   A__						,    A__											=					generate_predictions(_A ,	_A ,	_A )
   assert (
       len(_A ) == num_samples
   ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_A )}"""



def        __a							(			A = False ,	A = False )       ->						int:



   '''simple docstring'''



   A__											=					evaluate.load("glue" ,	"mrpc" )
   A__						,    A__											=					get_mrpc_setup(_A ,	_A )
   # First do baseline
   A__						,    A__						,    A__											=					setup["no"]
   model.to(_A )
   model.eval()
   for batch in dataloader:
      batch.to(_A )
      with torch.inference_mode():
         A__											=					model(**_A )
      A__											=					outputs.logits.argmax(dim=-1 )
      metric.add_batch(predictions=_A ,	references=batch["labels"] )
   A__											=					metric.compute()

   # Then do distributed
   A__						,    A__						,    A__											=					setup["ddp"]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         A__											=					model(**_A )
      A__											=					outputs.logits.argmax(dim=-1 )
      A__											=					batch["labels"]
      A__						,    A__											=					accelerator.gather_for_metrics((preds, references) )
      metric.add_batch(predictions=_A ,	references=_A )
   A__											=					metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key] ,	distributed[key] ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""



def        __a							(			)       ->						Optional[int]:



   '''simple docstring'''



   A__											=					Accelerator(split_batches=_A ,	dispatch_batches=_A )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("**Testing gather_for_metrics**" )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
            test_mrpc(_A ,	_A )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test torch metrics**" )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         A__											=					Accelerator(split_batches=_A ,	dispatch_batches=_A )
         if accelerator.is_local_main_process:
            print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
         test_torch_metrics(_A ,	99 )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("**Test last batch is not dropped when perfectly divisible**" )
   A__											=					Accelerator()
   test_torch_metrics(_A ,	512 )
   accelerator.state._reset_state()



def        __a							(			A )       ->						int:



   '''simple docstring'''



   main()


if __name__ == "__main__":
 main()



from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		CustomTokenizer
			pass


